class_docstr: Base class for RLlib's TensorFlow models.
  
  This class defines the interface for RLlib's TensorFlow models and checks
  whether inputs and outputs of __call__ are checked with `check_input_specs()` and
  `check_output_specs()` respectively.
class_docstr_tok: ["Base","class","for","RLlib","'s","TensorFlow","models",".","This","class","defines","the","interface","for","RLlib","'s","TensorFlow","models","and","checks","whether","inputs","and","outputs","of","__call__","are","checked","with","`","check_input_specs","(",")","`","and","`","check_output_specs","(",")","`","respectively","."]
code: def _set_to_dummy_weights(
      self, value_sequence=(-0.02, -0.01, 0.01, 0.02)
  ):
      for i, w in enumerate(
          self.trainable_weights + self.non_trainable_weights
      ):
          fill_val = value_sequence[i % len(value_sequence)]
          w.assign(tf.fill(w.shape, fill_val))
  
file_path: ray/rllib/core/models/tf/base.py
imports: import abc
  import logging
  from typing import Tuple
  import numpy
  from ray.rllib.core.models.base import Model
  from ray.rllib.core.models.configs import ModelConfig
  from ray.rllib.core.models.specs.checker import check_input_specs, is_input_decorated, is_output_decorated, check_output_specs
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.util import log_once
label_desc: Use image node_id 4 for calling the TfModel obj's underlying member method code with example usage: obj._set_to_dummy_weights(value_sequence) without return types